Fault Tolerance requirement, which demands the reliability and consistency in compute systems, becomes a hot issue in current distributed object application field. Based on standardized Portable Interceptors mechanism...
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ISBN:
(纸本)0780376242
Fault Tolerance requirement, which demands the reliability and consistency in compute systems, becomes a hot issue in current distributed object application field. Based on standardized Portable Interceptors mechanism, CARRIAGE system has successfully integrated ORB US, a CORBA implementation developed by the authors and EDEN, a fault-tolerant framework into a whole new Fault Tolerant CORBA system, which uses active replication style to enhance fault tolerance service in CORBA domain with low cost and high efficiency. Practice has identified that this software prototype conforms to the standard specification thoroughly and provides a feasible and convenient way to glue the legacy systems without modifying the original systems or the application programs either.
The future Internet is expected to support applications with quality of service (QoS) requirements. For this end several mechanisms are suggested in the IETF to support signaling, one of the most promising component a...
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The future Internet is expected to support applications with quality of service (QoS) requirements. For this end several mechanisms are suggested in the IETF to support signaling, one of the most promising component among them is IntServ. In this paper, we study the relationship between the QoS implementing mechanism and application demands, and make a set of an improved application interface beyond the standard resource reservation protocol (RSVP) application programming interface (API). Our goal is to provide an API friendly to applications, through extracting the key elements from the traffic parameters and reservation parameters which should be opaque to applications.
Currently the research on input-queued ATM switches is one of the most active research fields. Many achievements have been made in the research on scheduling algorithms for unicast input-queued ATM switches and also a...
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ISBN:
(纸本)0780367111
Currently the research on input-queued ATM switches is one of the most active research fields. Many achievements have been made in the research on scheduling algorithms for unicast input-queued ATM switches and also applied in commerce. But the goal of the research on scheduling algorithms for multicast input-queued ATM switches only focuses on providing high throughput and inadvertently ignoring its undesired effects on QoS of the multicast traffic. In this paper we present a design scheme of input-queued ATM switches supporting multicast and corresponding scheduling algorithm, referred to as multicast longest normalized queue first (MLNQF). The MLNQF algorithm has the characteristics of improving throughput, satisfying QoS requirements and providing service fairly.
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial...
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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.
Researching genes and their interactions is crucial for deciphering the fundamental laws of cellular activity, advancing disease treatment, drug discovery, and more. Large language Models (LLMs), with their profound t...
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Researching genes and their interactions is crucial for deciphering the fundamental laws of cellular activity, advancing disease treatment, drug discovery, and more. Large language Models (LLMs), with their profound text comprehension and generation capabilities, have made significant strides across various natural science fields. However, their application in cell biology remains limited and a systematic evaluation of their performance is lacking. To address this gap, in this paper, we select seven mainstream LLMs and evaluate their performance across nine gene-related problem scenarios. Our findings indicate that LLMs possess a certain level of understanding of genes and cells, but still lag behind domain-specific models in comprehending transcriptional expression profiles. Moreover, we have improved the current method of textual representation of cells, enhancing the LLMs’ ability to tackle cell annotation tasks. We encourage cell biology researchers to leverage LLMs for problem-solving while being mindful of the associated challenges. We release our code and data at https://***/epang-ucas/Evaluate_LLMs_to_Genes.
Multi-modal sarcasm detection involves determining whether a given multi-modal input conveys sarcastic intent by analyzing the underlying sentiment. Recently, vision large language models have shown remarkable success...
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Multi-modal sarcasm detection involves determining whether a given multi-modal input conveys sarcastic intent by analyzing the underlying sentiment. Recently, vision large language models have shown remarkable success on various of multi-modal tasks. Inspired by this, we systematically investigate the impact of vision large language models in zero-shot multi-modal sarcasm detection task. Furthermore, to capture different perspectives of sarcastic expressions, we propose a multi-view agent framework, S3 Agent, designed to enhance zero-shot multi-modal sarcasm detection by leveraging three critical perspectives: superficial expression, semantic information, and sentiment expression. Our experiments on the MMSD2.0 dataset, which involves six models and four prompting strategies, demonstrate that our approach achieves state-of-the-art performance. Our method achieves an average improvement of 13.2% in accuracy. Moreover, we evaluate our method on the text-only sarcasm detection task, where it also surpasses baseline approaches.
The recent breakthrough in wireless power transfer technology enables power to be delivered between transceivers, which is quite helpful for sensor-cloud systems. Traditional charging schemes are only suitable for sta...
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The recent breakthrough in wireless power transfer technology enables power to be delivered between transceivers, which is quite helpful for sensor-cloud systems. Traditional charging schemes are only suitable for static sensors, while the issue of charging mobile sensors is ignored. In this paper, we make the first attempt to serve mobile sensors in the sensor-cloud systems in a “chasing” way, where a mobile charger can chase mobile sensors to replenish them. We formalize the charging utility MAximization Problem for dynamic sensors with a mobile charger (MAP) and propose a ChaseCharge algorithm based on RNN to solve it. Theoretical analyses are presented to explore the features of the proposed scheme. We carry out simulations, and the results show that the performance of our algorithm outperforms comparison algorithms by 33% in utility on average. Test-bed experiments are conducted to validate the applicability of the proposed scheme in oceanic monitoring applications.
Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-domain Sequential Recommendation (CSR),...
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Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-domain Sequential Recommendation (CSR), NCSR poses several challenges: 1) NCSR methods often rely on explicit item IDs, overlooking semantic information among entities. 2) Existing CSR mainly relies on domain alignment for knowledge transfer, risking semantic loss during alignment. 3) Most previous studies do not consider the many-to-one characteristic, which is challenging because of the utilization of multiple source domains. Given the above challenges, we introduce the prompt learning technique for Many-to-one Non-overlapping Cross-domain Sequential Recommendation (MNCSR) and propose a Text-enhanced Co-attention Prompt Learning Paradigm (TCPLP). Specifically, we capture semantic meanings by representing items through text rather than IDs, leveraging natural language universality to facilitate cross-domain knowledge transfer. Unlike prior works that need to conduct domain alignment, we directly learn transferable domain information, where two types of prompts, i.e., domain-shared and domain-specific prompts, are devised, with a co-attention-based network for prompt encoding. Then, we develop a two-stage learning strategy, i.e., pre-train & prompt-tuning paradigm, for domain knowledge pre-learning and transferring, respectively. We conduct extensive experiments on three datasets and the experimental results demonstrate the superiority of our TCPLP. Our source codes have been publicly released.
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